Plasticity Neural Network Based on Astrocytic Influence at Critical Period, Synaptic Competition and Compensation by Current and Mnemonic Brain Plasticity and Synapse Formation. (arXiv:2203.11740v3 [cs.NE] UPDATED)
The mechanism of our NN is very well in line with the results of the latest
MIT brain plasticity study, in which researchers found that as a synapse
strengthens, neighboring synapses automatically weaken themselves to
compensate. Regarding the importance of this mechanism, Dr. Luo's team at
Stanford University has put forward that competition regarding synapse
formation for dendritic morphogenesis is crucial. We try to conduct research on
the mechanism of failure in brain plasticity by model at the closure of
critical period in details by contrasting with studies before. Cutting edge
imaging and genetic tools are combined in their experimental studies, whereas
our research lays more emphasis on the model, derivation and simulation of a
new NN. In tests, which demonstrate that dendrite generation, to a certain
extent, is curbed by synapse formation. Current and mnemonic brain plasticity
as well as synaptic action range are also taken into account in the study.
Furthermore, the frame of the new NN is based on current gradient informational
and mnemonic negative and positive gradient informational synapse formation.
The mnemonic gradient information needs to take into account the forgotten
memory-astrocytic synapse formation memory persistence factor (including both
negative and positive memories - i.e. the optimal gradient information so far
and relatively inferior gradient information). We found that the astrocytic
memory persistence factor, like the phagocytosis factor, produces the effect of
reducing the local accumulation of synapses. The PNN in which only the synaptic
phagocytosis effect is considered regardless of the gradients update, and
whether the synaptic phagocytosis of different variables and synaptic positions
is cancelled is determined by the correlation coefficient of the corresponding
time interval, proves simple and effective.
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